Mechanisms of one-electron capture by slow He2+, C4+ and O6+ ions in collisions with CH4

Translational energy spectroscopy (TES) has been used to study one-electron capture by He2+, C4+, and O6+ ions in collisions with CH4 within the range 200 - 2000 eV amu—1. In each case the main collisions mechanisms and product channels have been identified. The measurements reveal significant differences in the way the dissociative and non-dissociative mechanisms contribute to electron capture. However, in all cases, the highly selective nature of the charge transfer process is confirmed in spite of the wide range of energy defects associated with possible product channels.

Rotation of a nanoparticle cloud in an inductively coupled plasma induced by weak static magnetic fields

Hydrocarbon nanoparticles with diameters between 10 and 30 nanometres are created in a low pressure plasma combining capacitive and inductive power coupling. The particles are generated in the capacitive phase of the experiment and stay confined in the plasma in the inductive phase. The presence of these embedded particles induces a rotation of a particle-free region (void) around the symmetry axis of the reactor. The phenomenon is analysed using optical emission spectroscopy both line integrated and spatially resolved via an intensified charge coupled device camera. From these data, electron temperatures and densities are deduced. We find that the rotation of the void is driven by a tangential component of the ion drag force induced by an external static magnetic field. Two modes are observed: a fast rotation of the void in the direction opposite to that of the tangential component and a slow rotation in the same direction. The rotation speed decreases linearly with the size of the particles. In the fast mode the dependence on the applied magnetic field is weak and consequently the rotation speed can serve as a monitor to detect particle sizes in low temperature plasmas.

Improved Process Monitoring Using Nonlinear Principal Component Models

This paper presents two new approaches for use in complete process monitoring. The firstconcerns the identification of nonlinear principal component models. This involves the application of linear
principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented.

Ionization of Helium and Argon by Very Slow Antiproton Impact

The total cross sections for single ionization of helium and single and double ionization of argon by antiproton impact have been measured in the kinetic energy range from 3 to 25 jeVusing a new technique for the creation of intense slow antiproton beams. The new data provide benchmark results for the development of advanced descriptions of atomic collisions and we show that they can be used to judge, for the first time, the validity of many recent theories.